Summary of Can Large Language Models Be Trusted For Evaluation? Scalable Meta-evaluation Of Llms As Evaluators Via Agent Debate, by Steffi Chern et al.
Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
by Steffi Chern, Ethan Chern, Graham Neubig, Pengfei Liu
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes ScaleEval, a framework for reliably evaluating Large Language Models (LLMs) as evaluators across diverse tasks and scenarios. Existing evaluation approaches often rely on LLMs to assess responses generated by other LLMs, but this meta-evaluation is typically limited by the coverage of existing benchmarks or requires extensive human annotation. To address this challenge, ScaleEval leverages multiple communicative LLM agents in multi-round discussions to assist human annotators in discerning the most capable LLMs as evaluators. This framework significantly eases the workload of human annotators and can be applied to new, user-defined scenarios. The proposed method is designed for scalability and efficiency, enabling reliable evaluation of LLMs across various contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in artificial intelligence research. Right now, it’s hard to tell which Large Language Models are the best at evaluating other models. This is important because we need good evaluators to make sure our AI systems are working well. The authors propose a new way to evaluate these models called ScaleEval. It uses multiple AI agents to have discussions and help humans decide which models are the best. This makes it much easier for humans to do their job, and it can be used in all sorts of different situations. |
